Selective redaction of media content

ABSTRACT

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for identifying and documenting certain subject matter found in media content, as well as selectively redacting the subject matter found in media content. In accordance with various aspects, a method is provided that comprises: obtaining first metadata for media content, the metadata identifying a context and a portion of content for an individual; identifying, based on the context, a certain subject matter for the media content; determining that a particular item associated with the subject matter is present in the portion of content associated with the individual; generating second metadata to document the particular item present in the portion of content; and using the second metadata to selectively redacting the item from the portion of content associated with the individual upon request to do so with respect to the individual.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Patent Application Ser. No. 63/150,650, filed Feb. 18, 2021, the entire disclosure of which is hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure is generally related to systems and methods for identifying and documenting human perceptible elements of electronic information that are gathered, associated, created, formatted, edited, prepared, or otherwise processed in forming a unified collection of such information storable as a distinct entity such as media content.

BACKGROUND

The volume of media content created, managed, and/or made available by entities (e.g., organizations) has continued to grow dramatically over recent years. In general, media content involves using different forms of content such as text, audio, still images, animation, video, and/or the like, or any combination thereof (sometimes referred to multimedia content). Reasons for such growth can be contributed to the fact that, although text can be viewed as a form of media content, other forms of media content such as audio and video can be much more user-friendly for many applications. In addition, computing power and network capacity have continued to increase, and costs associated with these technologies have decreased, over time, further contributing to the use of media content other than content that is simply text-based.

However, a technical challenge often encountered by entities (e.g., organizations) in utilizing media content for various applications is that its increased use has led to a higher likelihood of inclusion of certain subject matter (e.g., personal data, sensitive information, etc.) in the content that can pose a significant risk to an individual whose subject matter may be exposed through the media content. For example, a significant risk to including an individual's personal data in media content is that the personal data can be exposed in a manner that can lead to a data-related incident such as the personal data being stolen, or otherwise exposed to unintended parties. Therefore, many entities will go through the process of having the certain subject matter removed (e.g., redacted) from the media content.

However, simply redacting certain subject matter from media content is not always desirable because the subject matter may be needed or wanted for particular applications. Therefore, it can be particularly technically challenging for many entities to utilize media content that may contain certain subject matter for some applications, while redacting the subject matter for other applications and/or associated with particular individuals to minimize the risk of exposure. Therefore, a need exists in the arts for identifying and documenting occurrences of certain subject matter in media content and redacting the subject matter found in the media content for particular applications and/or related to particular individuals. Accordingly, various aspects of the disclosure provided herein address such a need.

SUMMARY

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for identifying and documenting certain subject matter found in media content, as well as redacting the subject matter found in media content for particular applications and/or associated with particular individuals. In accordance with various aspects, a method is provided. Accordingly, the method comprises: obtaining, by computing hardware, first metadata for media content comprising a first portion of audio content of a first individual involved in the media content and a second portion of audio content of a second individual involved in the media content, wherein the first metadata identifies (1) a context for the media content, (2) the first portion of audio content is associated with the first individual, and (3) the second portion of audio content is associated with the second individual; identifying, by the computing hardware and based on the context, a certain subject matter for the media content; for the first portion of audio content: segmenting, by the computing hardware, the first portion of audio content into a first sequence of audio content segments; processing, by the computing hardware, each audio content segment of the first sequence of audio content segments using a multilabel classification machine-learning model to generate a first feature representation comprising a plurality of components for the audio content segment, wherein each component of the plurality of components represents a particular keyword associated with the subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, by the computing hardware and based on the prediction provided for a particular component of the plurality of components generated for a first particular audio content segment of the first sequence of audio content segments, that the particular keyword associated with the subject matter is present in the first particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the first particular audio content segment, generating, by the computing hardware, a first identifier for the first particular audio content segment that identifies the particular keyword associated with the subject matter is present in the first particular audio content segment; for the second portion of audio content: segmenting, by the computing hardware, the second portion of audio content into a second sequence of audio content segments; processing, by the computing hardware, each audio content segment of the second sequence of audio content segments using the multilabel classification machine-learning model to generate a second feature representation comprising the plurality of components for the audio content segment, wherein each component of the plurality of components represents the particular keyword associated with the subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, by the computing hardware and based on the prediction provided for a particular component of the plurality of components generated for a second particular audio content segment of the second sequence of audio content segments, that the particular keyword associated with the subject matter is present in the second particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the second particular audio content segment, generating, by the computing hardware, a second identifier for the second particular audio content segment that identifies the particular keyword associated with the subject matter is present in the second particular audio content segment; and generating, by the computing hardware, second metadata for the media content, wherein the second metadata comprises the first identifier and the second identifier.

In some aspects, processing each audio content segment of the first sequence of audio content segments using the multilabel classification machine-learning model to generate the first feature representation comprises: processing the audio content segment using a speech-to-text engine to generate a text representation of the audio content segment, the text representation comprising a sequence of words spoken in the audio content segment; processing the text representation using the multilabel classification machine-learning model to generate the first feature representation.

In some aspects, the method further comprising: receiving a first request for the media content, wherein the first request identifies the first individual; identifying, by the computing hardware based on the first request identifying the first individual, the first identifier in the second metadata for the media content; segmenting, by the computing hardware, the first portion of audio content into the first sequence of audio content segments; generating, by the computing hardware based on the first identifier, a first redacted audio content segment for the first particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the first particular audio content segment is absent from the first redacted audio content segment; reassembling, by the computing hardware, the first sequence of audio content segments with the first particular audio content segment replaced with the first redacted audio content segment to generate a first portion of redacted audio content for the media content; and responsive to the first request, providing, by the computing hardware, the media content comprising the first portion of redacted audio content, wherein playing the media content comprising the first portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment not being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment being heard spoken by the second individual.

In some aspects, the method further comprises identifying, by the computing hardware and based on the first metadata and the first request identifying the first individual, the first portion of audio content of the media content. In some aspects, generating the first redacted audio content segment comprises at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment. In some aspects, generating the first redacted audio content segment comprises: processing the first particular audio content segment using a rules-based model to generate a redaction action, the redaction action involving at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment; and performing the redaction action to generate the first redacted audio content segment.

In some aspects, the method further comprises: receiving a second request for the media content, wherein the second request identifies the second individual; identifying, by the computing hardware based on the second request identifying the second individual, the second identifier in the second metadata for the media content; segmenting, by the computing hardware, the second portion of audio content into the second sequence of audio content segments; generating, by the computing hardware based on the second identifier, a second redacted audio content segment for the second particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the second particular audio content segment is absent from the second redacted audio content segment; reassembling, by the computing hardware, the second sequence of audio content segments with the second particular audio content segment replaced with the second redacted audio content segment to generate a second portion of redacted audio content for the media content; and responsive to the second request, providing, by the computing hardware, the media content comprising the second portion of redacted audio content, wherein playing the media content comprising the second portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment not being heard spoken by the second individual.

In accordance with various aspects, a system is provided comprising a non-transitory computer-readable medium storing instructions and a processing device communicatively coupled to the non-transitory computer-readable medium. The processing device is configured to execute the instructions and thereby perform operations that comprise: obtaining first metadata for media content comprising a first portion of video content associated with a first individual involved in the media content and a second portion of video content associated with a second individual involved in the media content, wherein the first metadata identifies (1) a context for the media content, (2) the first portion of video content is associated with the first individual, and (3) the second portion of video content is associated with the second individual; identifying, based on the context, a certain subject matter for the media content; for the first portion of video content: segmenting the first portion of video content into a first sequence of video content segments; processing each video content segment of the first sequence of video content segments using a multilabel classification machine-learning model to generate a first feature representation comprising a plurality of components for the video content segment, wherein each component of the plurality of components represents a particular object associated with the subject matter and provides a prediction as to whether the particular object associated with the subject matter is present in the video content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a first particular video content segment of the first sequence of video content segments, that the particular object associated with the subject matter is present in the first particular video content segment; and responsive to determining that the particular object associated with the subject matter is present in the first particular video content segment, generating a first identifier for the first particular video content segment that identifies the particular object associated with the subject matter is present in the first particular video content segment; for the second portion of video content: segmenting the second portion of video content into a second sequence of video content segments; processing each video content segment of the second sequence of video content segments using the multilabel classification machine-learning model to generate a second feature representation comprising the plurality of components for the video content segment, wherein each component of the plurality of components represents the particular object associated with the subject matter and provides a prediction as to whether the particular object associated with the subject matter is present in the video content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a second particular video content segment of the second sequence of video content segments, that the particular object associated with the subject matter is present in the second particular video content segment; and responsive to determining that the particular object associated with the subject matter is present in the second particular video content segment, generating a second identifier for the second particular video content segment that identifies the particular object associated with the subject matter is present in the second particular video content segment; and generating second metadata for the media content, wherein the second metadata comprises the first identifier and the second identifier. In some aspects, determining that the particular object associated with the subject matter is present in the first particular video content segment is based on the prediction provided for the particular component satisfying a threshold.

In some aspects, the operations further comprise: receiving a first request for the media content, wherein the first request identifies the first individual; identifying, based on the first request identifying the first individual, the first identifier in the second metadata for the media content; segmenting the first portion of video content into the first sequence of video content segments; generating, based on the first identifier, a first redacted video content segment for the first particular video content segment, wherein the particular object associated with the subject matter that is present in the first particular video content segment is absent from the first redacted video content segment; reassembling the first sequence of video content segments with the first particular video content segment replaced with the first redacted video content segment to generate a first portion of redacted video content for the media content; and responsive to the first request, providing the media content comprising the first portion of redacted video content, wherein playing the media content comprising the first portion of redacted video content results in the particular object associated with the subject matter that is present in the first particular video content segment not being displayed and the particular object associated with the subject matter that is present in the second particular video content segment being displayed.

In some aspects, generating the first redacted video content segment comprises at least one of removing the particular object associated with the subject matter from the first particular video content segment, replacing the particular object associated with the subject matter in the first particular video content segment, or altering the particular object associated with the subject matter in the first particular video content segment. In some aspects, the first feature representation comprises a bounding box defined by a point, width, and height position for the particular object associated with the subject matter in the first particular video content segment and generating the first redacted video content segment comprises: performing, based on the particular object being located within the bounding box, at least one of removing the particular object associated with the subject matter from the first particular video content segment, replacing the particular object associated with the subject matter in the first particular video content segment, or altering the particular object associated with the subject matter in the first particular video content segment. In some aspects, generating the first redacted video content segment comprises replacing one or more frames of the first particular video content segment comprising the particular object associated with the subject matter with one or more replacement frames comprising altered content.

In some aspects, the operations further comprise: receiving a second request for the media content, wherein the second request identifies the second individual; identifying, based on the second request identifying the second individual, the second identifier in the second metadata for the media content; segmenting the second portion of video content into the second sequence of video content segments; generating, based on the second identifier, a second redacted video content segment for the second particular video content segment, wherein the particular object associated with the subject matter that is present in the second particular video content segment is absent from the second redacted video content segment; reassembling the second sequence of video content segments with the second particular video content segment replaced with the second redacted video content segment to generate a second portion of redacted video content for the media content; and responsive to the second request, providing the media content comprising the second portion of redacted video content, wherein playing the media content comprising the second portion of redacted video content results in the particular object associated with the subject matter that is present in the first particular video content segment being shown and the particular object associated with the subject matter that is present in the second particular video content segment not being shown.

In addition in accordance with various aspects, a non-transitory computer-readable medium having program code that is stored thereon. The program code executable by one or more processing devices performs operations that comprise: obtaining first metadata for media content comprising first portion of audio content of a first individual involved in the media content and second portion of audio content of a second individual involved in the media content, wherein the first metadata identifies (1) the first portion of audio content is associated with the first individual and (2) the second portion of audio content is associated with the second individual; for the first portion of audio content: segmenting the first portion of audio content into a first plurality of audio content segments; processing each audio content segment of the first plurality of audio content segments using a multilabel classification machine-learning model to generate a first feature representation comprising a plurality of components for the audio content segment, wherein each component of the plurality of components represents a particular keyword associated with certain subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a first particular audio content segment of the first plurality of audio content segments, that the particular keyword associated with the subject matter is present in the first particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the first particular audio content segment, generating a first identifier for the first particular audio content segment that identifies the particular keyword associated with the subject matter is present in the first particular audio content segment; for the second portion of audio content: segmenting the second portion of audio content into a second plurality of audio content segments; processing each audio content segment of the second plurality of audio content segments using the multilabel classification machine-learning model to generate a second feature representation comprising the plurality of components for the audio content segment, wherein each component of the plurality of components represents the particular keyword associated with the subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a second particular audio content segment of the second plurality of audio content segments, that the particular keyword associated with the subject matter is present in the second particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the second particular audio content segment, generating a second identifier for the second particular audio content segment that identifies the particular keyword associated with the subject matter is present in the second particular audio content segment; and generating second metadata for the media content, wherein the second metadata comprises the first identifier and the second identifier.

In some aspects, processing each audio content segment of the first plurality of audio content segments using the multilabel classification machine-learning model to generate the first feature representation comprises: processing the audio content segment using a speech-to-text engine to generate a text representation of the audio content segment, the text representation comprising a sequence of words spoken in the audio content segment; and processing the text representation using the multilabel classification machine-learning model to generate the first feature representation.

In some aspects, the operations further comprise: receiving a first request for the media content, wherein the first request identifies the first individual; identifying, based on the first request identifying the first individual, the first identifier in the second metadata for the media content; segmenting the first portion of audio content into the first plurality of audio content segments; generating, based on the first identifier, a first redacted audio content segment for the first particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the first particular audio content segment is absent from the first redacted audio content segment; reassembling the first plurality of audio content segments with the first particular audio content segment replaced with the first redacted audio content segment to generate a first portion of redacted audio content for the media content; and responsive to the first request, providing the media content comprising the first portion of redacted audio content, wherein playing the media content comprising the first portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment not being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment being heard spoken by the second individual.

In some aspects, generating the first redacted audio content segment comprises at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment. In some aspects, generating the first redacted audio content segment comprises: processing the first particular audio content segment using a rules-based model to generate a redaction action, the redaction action involving at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment; and performing the redaction action to generate the first redacted audio content segment.

In some aspects, the operations further comprise: receiving a second request for the media content, wherein the second request identifies the second individual; identifying, based on the second request identifying the second individual, the second identifier in the second metadata for the media content; segmenting the second portion of audio content into the second plurality of audio content segments; generating, based on the second identifier, a second redacted audio content segment for the second particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the second particular audio content segment is absent from the second redacted audio content segment; reassembling the second plurality of audio content segments with the second particular audio content segment replaced with the second redacted audio content segment to generate a second portion of redacted audio content for the media content; and responsive to the second request, providing the media content comprising the second portion of redacted audio content, wherein playing the media content comprising the second portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment not being heard spoken by the second individual.

BRIEF DESCRIPTION OF THE DRAWINGS

In the course of this description, reference will be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 depicts an example of a computing environment that can be used for identifying and redacting certain subject matter found in media content in accordance with various aspects of the present disclosure;

FIG. 2 depicts an example of a process for generating metadata for media content with respect to occurrences of certain subject matter in the media content in accordance with various aspects of the present disclosure;

FIG. 3 depicts an example of a process for analyzing media content to identify and document certain subject matter in the media content in accordance with various aspects of the present disclosure;

FIG. 4 depicts an example of a process for identifying occurrences of certain subject matter found in media content in accordance with various aspects of the present disclosure;

FIG. 5 depicts an example of a process for selectively redacting certain subject matter from media content in accordance with various aspects of the present disclosure;

FIG. 6 depicts an example of a process for redacting occurrences of certain subject matter found in media content in accordance with various aspects of the present disclosure;

FIG. 7 depicts an example of a system architecture that may be used in accordance with various aspects of the present disclosure; and

FIG. 8 depicts an example of a computing entity that may be used in accordance with various aspects of the present disclosure.

DETAILED DESCRIPTION Overview and Technical Contributions of Various Aspects

As previously noted, it can be technically challenging for many entities (e.g., organizations) in utilizing media content that contains certain subject matter for particular applications, but then redacting the subject matter for other applications. As a specific example, an entity may be a healthcare provider that provides an interactive website that patients can visit and furnish medical information in setting up appointments with various medical personnel such as doctors. The interactive website may be beneficial in that it can allow for patients to provide information upfront before visiting a facility for the healthcare provider so that the patents are scheduled for appointments with the appropriate medical personal. The interactive website may involve a patient furnishing medical information such as symptoms the patient is experiencing, uploading images, answering questions presented by a chatbot, recording a video, and/or the like. Accordingly, the healthcare provider can record the entire interactive session so that it can be provided to the medical personnel in helping him or her prepare for the patient's visit.

However, the media content generated from the session may include other information that may be collected from the patient during the interactive session. For example, the patient may provide health insurance information that can then be used in billing for the services received by the patient. Therefore, the healthcare provider may also wish to provide the media content to billing personnel so that they may gather the patient's health insurance information from the media content. However, the billing personnel does not necessarily need to be privy to the patient's medical information (e.g., symptoms) provided during the interactive session. Therefore, the healthcare provider may wish to redact the medical information from the media content before sending it to the billing personnel.

This can present a technical challenge to the healthcare provider in that the medical information is desired to be included in the media content for some applications and is desired to be redacted from the media content for other applications. The healthcare provider may create two versions of the media content with a first version of the media content that includes the medical information and a second version of the media content that excludes the medical information. However, such an approach requires the healthcare provider to generate and manage multiple versions of the media content, as well as security store the multiple versions. This can lead to inefficient use of resources (both personnel and system) in generating and managing the multiple versions of the media content, as well as efficient use of resources in storing the multiple versions.

Such a situation can pose a further technical challenge for the healthcare provider when the healthcare provider would like to redact particular portions (e.g., types) of the health information provided by a patient during the session. For example, although the billing personnel does not necessarily need to be privy to the patient's medical information, the billing personnel may still need to be privy to at least a portion of the medical information so that the billing personnel can correctly bill for the services to be received by the patient. Therefore, the healthcare provider may not only need to redact the medical information for some applications, but may also need to selectively redact certain portions of the medical information. Such an approach may not be preferable to the healthcare provider.

As another specific example, an entity may be an advertising agency that conducts product panels that involve participants (e.g., individuals) in a group setting providing feedback on various products. Here, each of the product panels may be conducted in a virtual setting in which the participants are involved in an audio or video conference session that is recorded. During any one of these sessions, certain subject matter may be recorded for the participants such as their contact information (e.g., email address), demographic information, preference information, and/or the like. The media content (e.g. recording) for any one of these sessions may be provided to various parties who use the information provided by the participants in further developing the products discussed during the session, developing advertising for the products, and/or the like.

However, particular participants in any one of the conference sessions may request to have certain subject matter not be provided to various parties who may use the recording of the conference session. For example, a particular participant may wish to have his email address not provided to a particular party who may receive the recording of the conference session. Therefore, the advertising agency may be required to redact the participant's email address before providing the media content for the conference session to the party.

This can present a technical challenge to the advertising agency in that some participants' subject matter is desired to be included in the media content and other participants' subject matter is desired to be redacted from the media content. The advertising agent may create multiple versions of the media content to address this issue. However, again, such an approach can lead to inefficient use of resources (both personnel and system) in generating and managing the multiple versions of the media content, as well as efficient use of resources in storing the multiple versions. Therefore, such an approach may not be preferable to the advertising agency.

Various aspects of the present disclosure overcome many of the technical challenges associated with identifying and documenting certain subject matter (e.g., sensitive, or personal data) found in media content. In addition, various aspects of the present disclosure overcome many of the technical challenges associated with redacting certain subject matter from media content for various applications and/or associated with various individuals without the need for generating and maintaining multiple versions of the media content. Specifically, various aspects are directed to a computer-implemented selective redaction process for analyzing various forms of content, such as audio and/or video content, to identify portions of the content that contain certain subject matter that may be subject to selective redaction and documenting occurrences of the subject matter in the portions as metadata for the content. Accordingly, a selective redaction computing system may be provided to carried out the selective redaction process. In some aspects, the selective redaction computing system carries out the selective redaction process by redacting the subject matter from the media content for a particular application, purpose, and/or the like. In addition, in some aspects, the selective redaction computing system carries out the selective redaction process by redacting certain types of the subject matter from the media content. Further, in some aspects, the selective redaction computing system carries out the selective redaction process by redacting the subject matter associated with a particular individual (e.g., participant) from the media content.

To identify the portions of the media content that contain the subject matter, the selective redaction computing system may reference first metadata for the media content to identify a context for the material provided in the media content. For example, the first metadata may indicate the media content involves a content center call between an agent and a party who called into the contact center to place an order for a product. Accordingly, the selective redaction computing system may use the context for the material in determining what types of subject matter may be found in the media content. For example, the selective redaction computing system may determine that personal data may be found in the call to the contact center such as a credit card number may be provided by the party who called into the contact center to place the order for the product. In some aspects, the selective redaction computing system may use the context in determining what subject matter to search for in the media content.

In addition, the selective redaction computing system may determine the individuals (e.g., the participants) who are associated with the media content. For example, the first metadata may identify that a particular individual is associated with a particular audio channel for audio content provided in the media content. In another example, the first metadata may identify that a particular individual is associated with a section of a display of video content or is associated with a segment of the video content provided in the media content. As a specific example, the media content may involve a video conference in which multiple participants took part in the conference and the first metadata may identify what section (e.g., portion) of the display of the video conference involves a particular participant. Therefore, the selective redaction computing system may identify occurrences of the subject matter in the media content for the individuals based on the information provided in the first metadata.

In some aspects, the selective redaction computing system may separate the media content into content segments (e.g., utterances within audio content, one or more frames within video content, etc.). For example, the first metadata may indicate that at a first audio channel of the media content provides audio content associated with a particular individual. Therefore, the selective redaction computing system may separate the audio content provided through the first channel in identifying certain subject matter found in the media content associated with the particular individual.

Here, the selective redaction computing system may analyze each content segment to identify whether the subject matter is present in the content segment. In some aspects, the selective redaction computing system conducts this analysis by processing each content segment using a multilabel classification machine-learning model to generate a feature representation that includes components representing different items associated with the subject matter. For example, the items may represent different objects displayed (e.g., that are visible) in video content. In another example, the items may represent different keywords spoken in audio content. Each component of the feature representation provides a prediction as to whether the item associated with the subject matter is present in the content segment.

If a determination is made that a content segment contains an item associated with the subject matter, then the selective redaction computing system generates an identifier for the content segment that identifies the content segment as having the item associated with the subject matter present in the content segment. In addition, the identifier may indicate the type of subject matter present in the content segment. For example, the identifier may indicate that credit card number is present (e.g., spoken) in the content segment. Further, the identifier may indicate the content segment is associated with a particular individual (e.g., participant) found in the media content. For example, the identifier may be associated with the particular audio channel for the media content. In another example, the identifier may be associated with a particular section of a display of video content provided in the media content or associated with a segment of the video content. Yet, in another example, the identifier may be associated with an identifier for the individual.

Once the content segments have been processed for the media content to identify the occurrences of the subject matter in the segments, the selective redaction computing system continues with generating second metadata for the media content that includes the identifiers for the various content segments that have been identified as having the subject matter present in the content segments. Accordingly, the selective redaction computing system can then use the second metadata in performing selective redaction of the subject matter in the media content.

In some aspects, the selective redaction computing system redacts the subject matter from the media content based on the media content being used for a particular purpose or application, providing the media content to a particular party, and/or the like. In some aspects, the selective redaction computing system redacts certain types of the subject matter from the media content based on the media content being used for a particular purpose or application, providing the media content to a particular party, and/or the like. In other aspects, the selective redaction computing system redacts the subject matter from the media content associated with a particular individual (e.g. participant) involved in the media content. Accordingly, the selective redaction computing system may make use of the second metadata in carrying out the redaction of the subject matter from the media content.

In some aspects, the selective redaction computing system uses one or more various techniques in redacting the item from the content segment. For example, such techniques may include: (1) removing the item associated with the subject matter entirely (e.g., removing one or more particular words associated with the subject matter from the audio content segment or removing objects associated with the subject matter from the video content segment); (2) replacing the item associated with the subject matter with content that is not directed to the subject matter (e.g., replacing one or more frames containing the item found in the video content segment with a blank image); (3) altering the content segment so that the item associated with the subject matter is not recognizable (e.g., altering the audio content segment so that one or more words directed to the subject matter are distorted, altering frames within the video content segment with frames that are at least partially distorted so that the item associated with the subject matter is no longer visible within the frames, etc.); or (4) replacing the entire content segment with a replacement content segment not having the item.

The selective redaction computing system continues with reassembling the content segments (e.g., in the correct sequence) to generate a redacted sequence of content segments. For example, the media content may involve video content and/or audio content. Therefore, the selective redaction computing system can reassemble the sequence of video content segments with the particular video content segments having the subject matter replaced with the redacted video content segments to generate a redacted sequence of video content segments. Likewise, the selective redaction computing system can reassemble the sequence of audio content segments with the particular audio content segments having the subject matter replaced with the redacted audio content segments to generate a redacted sequence of video content segments. In some aspects, the selective redaction computing system combine the redacted sequence of video content segments and the redacted sequence of audio content segments to generate redacted media content.

The redacted media content can then be provided to an intended recipient so that the redacted media content may be consumed by the recipient without divulging the subject matter. For example, in some instances, the redacted media content may have been generated for a particular purpose or application, or redacted for a particular party. In some instances, the redacted media content may have been generated in addition, or instead of, with the subject matter redacted that is associated with a certain type of subject matter. In some instances, the redacted media content may have been generated in addition, or instead of, with the subject matter redacted that is associated with to a particular individual (e.g., participant) in the media content. Accordingly, the recipient of the redacted media content may discard the content once it has been used, if desired, since the selective redaction computing system can perform the selective redaction process on the original media content again using the second metadata.

As described herein, certain aspects of the selective redaction process provide improvements in customizing media content by facilitating selective redaction of certain subject matter by automatically generating and applying metadata, as well as applying various rules, such as subject matter redaction rules to control the manner in which the selective redaction computing system dynamically redacts various aspects of the media content. For example, some aspects may generate metadata identifying occurrences of certain subject matter that can be used by the selective redaction computing system in automatically performing selective redaction of the media content. In addition, some aspects may implement various rules to modify or configure an inclusion and/or exclusion of certain portions of the media content (e.g., by removing a portion of the content, obscuring a portion of the content, etc.). This process may reduce or eliminate the need to rely on user inputs (e.g., to identify portions of the media content to be redacted, redact the identified content, reconfigure the redacted content, etc.). The automated generation and application of the metadata, and application of these rules is facilitated by and specifically improves media content editing and redaction.

By contrast, conventional techniques for generating redacted media content require subjective determinations applied to imprecise manual operations, such as: manually reviewing segments of media content to identify portions of the media content that contain certain subject matter (e.g., sensitive, inappropriate, and/or undesirable subject matter); manually redacting, removing, replacing, or otherwise altering the subject matter; manually reconfiguring the redacted media content, etc. Thus, aspects described herein, improve the computer-implemented processes that are unique to redacting media content. Furthermore, aspects described herein provide improvements in computing systems used for redacting certain subject matter (e.g., sensitive, inappropriate, and/or undesirable subject matter) from media content by, for example, reducing cumbersome or time-consuming processes for ensuring that media content made available over a public data network (e.g., the internet) or other communication channel does not contain subject matter or other data such there would be a significant risk that the data would be exposed in a manner that results in a data loss incident (e.g., by virtue of the multimedia content being made available over a public data network. Further detail is now provided on various aspects.

Example Computing Environment

FIG. 1 depicts an example of a computing environment that can be used for selectively redacting certain subject matter from media content in some aspects. An entity (e.g., an organization) that is generating and/or using media content for various applications may be interested in selectively redacting certain subject matter from the content. For example, the entity may be interested in identifying occurrences of the subject matter in the media content so that the entity can then selectively redact the subject matter for particular purposes, applications, and/or the like. In addition, the entity may be interested in identifying occurrences of the subject matter that are associated with particular individuals (e.g., participants) so that the entity can then selectively redact the subject matter associated with a particular individual from the media content.

In some aspects, a selective redaction computing system 100 within the computing environment includes software components and/or hardware components for facilitating the selective redaction of certain subject matter found in media content. For instance, the selective redaction computing system 100 may provide a selective redaction service that is accessible over one or more networks 150 (e.g., the Internet) by an entity (e.g., a client computing system 160 associated with the entity). For example, the healthcare provider and/or advertising agency discussed in the examples provided in the overview may access the selective redaction service through client computing systems 160 associated with the healthcare provider and/or advertising agency that are in communication with the selective redaction computing system 100 over one or more networks 150.

Here, personnel of the entity may access the selective redaction service through one or more graphical user interfaces (e.g., webpages) provided by the selective redaction computing system 100. The personnel of the entity may use the service in performing an identification and documentation of certain subject matter found in media content. In addition, or instead, the personnel of the entity may use the service in performing a selective redaction of the subject matter found in the media content. The personnel may provide the media content that is to be documented and/or redacted to the selective redaction computing system 100.

In some instances, the selective redaction computing system 100 may include one or more repositories 140 that can be used for storing the received media content. In other instances, the personnel of the entity may provide a location of the media content that the selective redaction computing system 100 can then access to perform the documentation and/or redaction process. For example, the media content may be stored on some type of media source 170 that is accessible by the selective redaction computing system 100 over one or more networks 150. Here, the personnel of the entity may provide the location of the media content on the media storage along with credentials to gain access to the media content.

In some aspects, a documentation module 110 executed by the selective redaction computing system 100 is used in facilitating the identification and documentation of occurrences of certain subject matter found in media content. As discussed further herein, the documentation module 110 analyzes the media content to identify occurrences of the subject matter and generates metadata for the media content providing identifiers for the occurrences. In addition, the documentation module 110 may associated the occurrence of the subject matter with particular types of the subject matter. Further, the documentation module 110 may associate the occurrences of the subject matter with various individuals (e.g., participants) who are involved in the media content.

In additional or alternative aspects, a process media module 115 is provided by the selective redaction computing system 100. The process media module 115 is used in facilitating the analysis of the media content to identify the occurrences of the subject matter in the media content. In some aspects, the process media module 115 processes portions of the media content that are associated with individuals separately so that the occurrences of the subject matter can be associated with the individuals who are involved in the media content.

For example, if the media content involves audio content, the process media module 115 may process the audio content provided on different audio channels of the media content. Each audio channel may be associated with a particular individual involved in the media content who used the audio channel in speaking on the media content. Therefore, the process media module 115 can process the audio content for each of the audio channels separately to identify occurrences of the subject matter for each of the individuals who are involved in the media content. Accordingly, the documentation module 110 may invoke the process media module 115 to analyze the media content to identify the occurrences of the subject matter in the media content.

In some aspects, an occurrence module 120 executed by the selective redaction computing system 100 performs the actual identification and documentation of the occurrences of subject matter in the media content. Accordingly, the process media module 115 may invoke the occurrence module 120 to perform the identification and documentation of the occurrences. The occurrence module 120 segments the media content into a sequence of media content segments that are then processed to identify the occurrences of the subject matter in the segments. For example, the occurrence module 120 may segment video content into a sequence of video content segments that are made up of one or more video frames from the video content. In another example, the occurrence module 120 may segment audio content into a sequence of audio content segments that are made up on one or more utterances that are spoken by individuals in the audio content. The occurrence module 120 may then process each media content segment using a multilabel classification machine-learning model to determine whether the media content segment contains the subject matter.

For instance, if the media content is video content, then the occurrence module 120 may process each video content segment using the multilabel classification machine-learning model to determine whether the video content segment contains an object associated with the subject matter. For example, the object may be a sign displaying the subject matter. If the media content is audio content, then the occurrence module 120 may process each audio content segment using the multilabel classification machine-learning model to determine whether the audio content segment contains a keyword associated with the subject matter. For example, a keyword may be a single word or group of words (e.g., phrase) spoken by an individual in the audio content that is directed to the subject matter.

In some aspects, the multilabel classification machine-learning model may generate a feature representation that includes components that represent different items that are associated with the subject matter. Each component provides a prediction (e.g., a prediction value) as to whether the corresponding item associated with the subject matter is present in the media content segment. For example, each component may provide a prediction as to whether a particular object associated with the subject matter is present in the video content or each component may provide a prediction as to whether a particular keyword associated with the subject matter is present in the audio content segment. Accordingly, the occurrence module 120 can use the predictions provided for the different components in identifying which of the items associated with the subject matter is present in the media content segment.

As discussed in further detail herein, the selective redaction computing system 100 may make multiple classifiers for the multilabel classification machine-learning model available to entities through the selective redaction service so that the entities can perform redaction on media content that involves different types of subject matter. For example, classifiers may be provided through the service that can be used in identifying occurrences of certain types of sensitive and/or personal information such as: (1) first and/or last name; (2) home address; (3) email address; (4) social security number; (5) medical history; (6) username; (7) password; (8) account number; (9) and/or the like. In addition, classifiers may be provided for the different forms of media content. For example, classifiers may be provided for video content. Further, classifiers may be provided for audio content.

In some aspects, an entity (e.g., personnel thereof) wishing to perform the documentation portion of the selective redaction process (documentation process) on particular media content may select, from the available classifiers, one or more classifiers to be used by the multilabel classification machine-learning model in generating predictions for particular items associated with certain subject matter being present in the media content. In other aspects, the selective redaction computing system 100 may determine which of the classifiers is to be used by the multilabel classification machine-learning model. For example, the selective redaction computing system 100 may determine, based on metadata provided for the particular media content, a context for the media content. For example, the selective redaction computing system 100 may determine that individuals' medical histories may be included in the media content. Therefore, the selective redaction computing system 100 may use the context in determining which of the classifiers is to be used by the multilabel classification machine-learning model.

Once the occurrence module 120 has identified an occurrence of an item in a media content segment (e.g., an object shown in a video content segment, or a keyword spoken in an audio content segment), then the occurrence module 120 documents the occurrence of the item for the media content by, for example, generating an identifier for the occurrence. Here, the occurrence module 120 may generate an identifier for the occurrence that also identifies the type of subject matter that is associated with the item identified for the occurrence. For example, the identifier may indicate that the item involves an individual's social security number. In addition, the occurrence module 120 may generate an identifier for the occurrence that also identifies which individual (participant) involved in the media content is associated with the occurrence.

Once all of the media content segments have been processed to identify and document the occurrences of the subject matter found in the segments, the process media module 115, in cooperation with the occurrence module 120, processes a next portion of media content that is associated with another individual in the same manner to identify and document the occurrences of the subject matter associated with the individual for the media content. Once the portions of media content for all the individuals have been analyzed, the documentation module 110 generates metadata for the media content that includes the identifiers for the different occurrences of the subject matter. Accordingly, the selective redaction computing system 100 can then use the metadata during a redaction portion of the selective redaction process (redaction process) in performing selective redaction of the subject matter from the media content.

For example, the entity may be interested in redacting the subject matter from the media content for a particular individual who is involved in the media content. Personnel for the entity may access the selective redaction service via a client computing system 160 over one or more networks 150 and submit a request for having the subject matter redacted from the media content. The request may provide certain information for conducting the redaction. For example, the request may identify the particular individual, or portion of the media content that is associated with the particular individual. In addition, the request may identify a type of subject matter that the entity would like to have redacted from the media content. For example, the request may identify that the entity would like to have the individual's medical history redacted from the media content. Further, the request may provide metadata for the media content. For example, the request may include metadata providing information on the media content that can be used in identifying the portion of the media content (e.g., audio channel) associated with the individual. In addition, the request may include metadata providing the identifiers for the occurrences of the subject matter documented for the media content.

In additional or alternative aspects, a redact media module 125 is provided by the selective redaction computing system 100. The redact media module 125 receives the request submitted by the entity to redact the subject matter from the media content and, based on the information provided in the request, redacts the media content accordingly. Therefore, the redact media module 125 references metadata for the media content in performing the selective redaction on the media content. As noted, the metadata may include the metadata generated by the documentation module 110 in documenting the occurrences of the subject matter in the media content. The metadata may also include information identify the portion of the media content that is associated with the individual.

In some aspects, a redaction module 130 is invoked by the redact media module 125 and executed by the selective redaction computing system 100 to perform the selective redaction of the subject matter from the media content. Similar to the occurrence module 120, the redaction module 130 segments the media content into a sequence of media content segments that are then processed to redact the subject matter that may be found in the segments. The redaction module 130 processes each media content segment based on the identifiers provided in the metadata for the media content. The redaction module 130 references the identifiers provided in the metadata in determining whether subject matter that needs to be redacted is present in the media content segment.

Once the redaction module 130 identified a media content segment that includes an item associated with subject matter that needs to be redacted (e.g., an object shown in a video content segment or a keyword spoken in an audio content segment that need to be redacted), the redaction module 130 redacts the item from the media content segment to generate a redacted media content segment. The redaction module 130 may perform this operation by using any number of different techniques such as, for example: (1) removing the item associated with the subject matter entirely (e.g., removing one or more words associated with the subject matter from the audio content segment or removing the object associated with the subject matter from the video content segment); (2) replacing the item associated with the subject matter in the media content segment with content that is not directed to the subject matter (e.g., replacing one or more frames containing the object found in the video content segment with a blank image); (3) altering the media content segment so that the item associated with the subject matter is not recognizable (e.g., altering the audio content segment so that one or more words directed to the subject matter are distorted, replacing frames within the video content segment having the object with frames that are at least partially distorted so that the object is no longer visible within the frames, etc.); or (4) replacing the entire media content segment with a replacement media content segment not having the item.

Once all of the media content segments have been processed to redact any occurrences of the subject matter found in the segments that need to be redacted, the redaction module 130 reassembles the media content segments to generate a redacted sequence of the media content segments. Here, the redaction module 130 may replace each media content segment found to contain subject matter that needs to be redacted with its corresponding redacted media content segment in the sequence of the media content segments.

At this point, the redact media module 125 may process additional portions of the media content that may also have subject matter that needs to be redacted based on the request. Once the redact media module 125 has processed all the portions of the media content to generate their corresponding portions of redacted media content, the redact media module 125 may recombine the different portions of redacted media content to generated redacted media content. Further detail is now provided regarding the configuration and functionality of the documentation module 110, the process media module 115, the occurrence module 120, the redact media module 125, and the redaction module 130 in some aspects.

Documentation Module

Turning now to FIG. 2, additional details are provided regarding a documentation module 110 used for generating metadata for media content with respect to occurrences of certain subject matter in the media content in accordance with various aspects. Accordingly, the flow diagram shown in FIG. 2 may correspond to operations carried out, for example, by computing hardware found in the selective redaction computing system 100 as described herein, as the computing hardware executes the documentation module 110.

Personnel for an entity interested in having media content processed to identify and document certain subject matter found in the media content may access a selective redaction service provided through a selective redaction computing system 100. For example, the personnel may access the service through a website published by the selective redaction computing system 100 over one or more networks 150 (such as the Internet). The website may provide the personnel with one or more webpages (e.g., graphical user interfaces) to facilitate conducting a documentation process on the media content. The media content may involve one or more forms of media content such as, for example, text, audio, still images, animation, video, and/or the like. The personnel may provide the media content that is to be documented directly to the selective redaction computing system 100 or may provide a location of the media content stored on a media source 170 that the selective redaction computing system 100 then accesses.

The process 200 involves the documentation module 110 retrieving metadata for the media content in Operation 210. For example, the metadata may be provided as part of the media content that is to be documented. The documentation module 110 may analyze the metadata to determine what forms of content are present for the media content. As a specific example, the metadata provided for the media content may indicate that the media content involves both video and audio content. Here, the metadata may provide various data on the different forms of content such as, for example, frame count, sampling rate, bit rate, file size, duration, and/or the like for video content (e.g., for video data provided in the media content file). Likewise, the metadata may provide audio channels, sampling rate, bit rate, file size, duration, and/or the like for audio content (e.g., for audio data provided in the media content file).

Further, the metadata may provide information on individuals who are involved in the media content. For example, the media content may include audio content. Here, the audio content may be provided on multiple audio channels. Therefore, the metadata may identify which audio channel is associated with each individual. In another example, the media content may include video content. Here, the video content may be structured to display individuals, or items associated with individuals, on particular portions of a display of the video content. As a specific example, the video content may involve a video conference in which individuals who took part in the video conference were displayed on particular sections (portions) of the display. Therefore, the metadata may identify which individuals were displayed on which sections of the display during the video conference. As another specific example, the metadata may identify the sections of video content associated with an individual by identifying certain frames of the video content that involve the individual, certain times of the video content that involve the individual, and/or the like. In some aspects, the personnel may provide the information on the individuals who are involved in the media content.

The remainder of the disclosure is described in the context of redacting media content involving video and audio content. But various aspects can be used in redacting other forms of media content. For example, various aspects can be used in redacting text, still images, animation, and/or the like.

In Operation 215, the documentation module 110 determines the types of target data to be identified and documented for the media content. In some aspects, the selective redaction computing system 100 may provide the personnel with options to identify what types of target data the entity wishes to identify and document for the media content. For example, the selective redaction computing system 100 may provide the personnel with categories of subject matter to select via one or more GUIs accessible through the selective redaction service. As a specific example, the selective redaction computing system 100 may provide the personnel with the options of selecting: (1) personal data of individuals; (2) sensitive data such as competitor names, advertising, trade secret references; (3) inappropriate language; (4) and/or the like. In another example, the selective redaction computing system 100 may provide the personnel with different types of subject matter within a category to select from via the one or more GUIs. As a specific example, the selective redaction computing system 100 may provide the personnel with the options within the category of personal data of selecting: (1) images of individuals; (2) first names; (3) last names; (4) email addresses; (5) medical histories; (6) residential addresses; (7) demographical information; (8) and/or the like. In addition, the selective redaction computing system 100 may provide the personnel with options for different forms of media content.

In other aspects, the documentation module 110 may determines the types of target data to be identified and documented for the media content based on a context associated with the media content. For example, the metadata provided for the media content may include information identifying a context for the media content. As a specific example, the metadata may identify the media content involves a video conference conducted between a doctor and a patient to discuss a medical issue the patient is experiencing. As another specific example, the metadata may identify the media content involves a contact center call between an agent and a caller who called the content center to receive technical support on an issue the caller is having with a product he or she purchases. Therefore, the documentation module 110 may determine, based on the metadata, a context for the media content (e.g., medical, commerce, etc.) and, based on the context, the types of target data (e.g., personal data, medical history, contact information, etc.) to be identified and documented for the media content.

In some instances, the documentation module 110 may have access to a data model that provides information on types of target data that are handled by the entity. As a specific example, the entity may be an e-commerce business that runs a web site through which visitors to the web site can purchase various products provided by the website. Functionality may be provided on the website that allows visitors to conduct audio chat sessions with agents to discuss different products and/or to facilitate purchases of the different products. Such chat sessions may be recorded. During a chat session, a visitor may provide various types of target data that can be personal and/or sensitive in nature. For example, the visitor may provide his or her credit card information to the agent handling the session in purchasing a product. The target data gathered during a chat session may be handled by various components found within a client computing system 160 of the entity. For example, the credit card information provided by a visitor may be received by a Web server, processed by an application server, transfer through a router, stored in a repository, and/or the like found within the client computing system 160. Therefore, the entity may develop a data model that defines the various components found in the client computing system 160 and the types of target data each of the components handles.

For example, a data model may define the various data sources, applications, processing activities, and/or the like as data assets found within the client computing system 160 that are involved in handling the target data. In addition, the data model may map relationships between and/or among the data assets. Further, the data model may define various attributes for the data assets. Such attributes for a data asset may include, for example: (1) one or more departments within an entity that are responsible for the data asset; (2) identification of types of target data handled by the data asset; (3) purposes for handling the target data; (4) one or more particular individuals (or categories of individuals) associated with the target data handled by the data asset; (5) one or more personnel (e.g., particular individuals and/or types of individuals) that are permitted to access the target data; (6) which particular types of target data the personnel are allowed to access and use; (7) and/or the like.

Accordingly, the documentation module 110 can use such a data model in determining the types of target data to be identified and documented for the media content. In some instances, the documentation module 110 may use a combination of the metadata provided for the media content and the information provided in the data model in determining the types of target data to be identified and documented for the media content. For example, the documentation module 110 may use the metadata in identifying a context for the media content. The documentation module 110 may then use the data model in identifying the types of target data that are handled by the entity (e.g., handled by a client computing system 160 of the entity) that are associated with the context in determining the types of target data to be identified and documented for the media content.

If the media content includes multiple forms of content (e.g., video content and audio content), then the documentation module 110 separate the media content in Operation 220 into its individual forms. For instance, the documentation module 110 may separate the video content from the audio content that make up the media content. In some aspects, the documentation module 110 uses a media content editor software application to extract the audio and/or video content from the media content. For example, the media content may be provided in a file format that is a container having video data in a video coding format and audio data in an audio coding format. Therefore, the documentation module 110 may use the media content editor in separating out the video data and the audio data from the container.

At Operation 225, the documentation module 110 selects one of the forms of media content. At this point, in Operation 230, the documentation module 110 analyzes the form of media content to identify and document occurrences of the target data found in the form of media content based on the metadata and the options selected by the personnel. As discussed further herein, the documentation module 110 performs this operation by invoking the process media module 115. In turn, the process media module 115 processes the form of media content as indicated by the personnel to identify and document the occurrences of the subject matter in the form of media content. For example, the process media module 115 may process the form of media content to identify and document the occurrences of the subject matter based on the types of subject matter identified by the personnel or determined by the documentation module 110. In addition, the process media module 115 may process portions of the form of media content that are associated with different individuals who are involved in the media content separately to identify and document occurrences of the subject matter associated with the different individuals.

In some aspects, the documentation module 110 performing the analysis on the media content results in identifiers being generated for each occurrence of the subject matter in the form of media content. For example, an identifier may indicate where in the form of media content the occurrence of subject matter happens. In addition, the identifier may indicate who is associated with the occurrence. Further, the identifier may indicate what type of subject matter is associated with the occurrence.

At Operation 235, the documentation module 110 determines whether the media content includes another form of media content. If so, then the documentation module 110 returns to Operation 225 and selects the next form of media content to process as just discussed. Once the documentation module 110 has processed each form of media content, the documentation module 110 generates metadata that includes the identifiers generated for each form of media content at Operation 240.

The documentation module 110 may then provide the metadata to the personnel of the entity who is conducting the documentation process on the media content through the selective redaction service. The personnel may then store the metadata along with the media content. In some instances, the documentation module 110 may store the metadata within the selective redaction computing system 100 in a repository 140. Accordingly, the metadata generated for the media content may then be used in selectively redacting subject matter from the media content.

For example, the metadata may be used in redacting certain types of subject matter from the media content. As a specific example, the entity may wish to redact any medical history of individuals discussed in the audio content of the media content. Therefore, the entity may utilize the selective redaction service provided through the selective redaction computing system 100 to redact the occurrences of medical history discussed in the audio content. Accordingly, the selective redaction computing system 100 (e.g., the redact media module 125) may use the metadata in performing the selective redaction on the audio content of the media content.

In another example, the metadata may be used in redacting subject matter associated with certain individuals from the media content. As a specific example, the entity may wish to redact subject matter from the media content that is associated with a particular individual who is involved in the media content. Again, the entity may utilize the selective redaction service provided through the selective redaction computing system 100 to redact the occurrences of subject matter found in the audio content for the individual. Accordingly, the selective redaction computing system 100 (e.g., the redact media module 125) may use the metadata in performing the selective redaction on the media content to redact the occurrences of the subject matter involving the individual.

Process Media Module

Turning now to FIG. 3, additional details are provided regarding a process media module 115 used for processing media content to identify and document certain subject matter found in the media content in accordance with various aspects. Accordingly, the flow diagram shown in FIG. 3 may correspond to operations carried out, for example, by computing hardware found in the selective redaction computing system 100 as described herein, as the computing hardware executes the process media module 115.

In some aspects, the process media module 115 is invoked by the documentation module 110 and is provided with media content in some form. For example, the media content may be audio content or video content. In addition, the process media module 115 may also receive metadata along with the media content. Further, the process media module 115 may receive one or more indications of the type of subject matter to be identified and documented for the media content. For example, the process media module 115 may receive an indication to identify and document occurrences of personal data such as an individual's email address and credit card number. Furthermore, the process media module 115 may receive a request to identify occurrences of the subject matter with respect to individuals involved in the media content.

Accordingly, the process 300 involves the process media module 115 identifying the individuals in Operation 310 in response to receiving a request to identify occurrences of the subject matter with respect to the individuals. For example, the process media module 115 may reference the metadata in identifying the individuals involved in the media content and their corresponding portions of the media content. As a specific example, the media content may be audio content having multiple audio channels of audio content for different individuals who are speaking on the audio content. Here, the metadata may identify the different audio channels and who is associated with each of the channels. Therefore, the process media module 115 can reference the metadata in identifying the individuals and their corresponding audio channels found in the audio content.

As another specific example, the media content may be video content. Here, the metadata may identify the number of frames found in the video content and/or an amount of runtime for the video content. In addition, the metadata may identify individuals who are present in the video content and/or who have objects associated with the subject matter in the video content, along with an indicator for each individual of the fames and/or timestamps associated with the individual. Therefore, the process media module 115 can reference the metadata in identifying the individuals and their corresponding portions of the video media.

In Operation 315, the process media module 115 selects a portion of the media content associated with a particular individual. The process media module 115 then processes the portion of the media content to identify and document the occurrences of the subject matter found in the portion of the media content in Operation 320. In some aspects, the process media module 115 invokes an occurrence module 120 to perform this particular operation. In turn, the occurrence module 120 segments the portion of media content for the individual and then processes each content segment to determine whether the content segment contains an occurrence of the subject matter. For example, the occurrence module 120 may determine that a particular content segment contains a keyword associated with the subject matter or an object associated with the subject matter.

If the occurrence module 120 determines a particular content segment does contain an occurrence of the subject matter, then the occurrence module 120 generates an identifier for the content segment indicating that the content segment contains an occurrence of the subject matter. In addition, the identifier may indicate the type of subject matter that is found in the content segment. Accordingly, the occurrence module 120 processes each of the content segments for the portion of the media content to determine whether the content segment contains an occurrence of the subject matter.

In Operation 325, the process media module 115 determines whether another individual is involved in the media content. If so, then the process media module 115 returns to Operation 315 and selects the portion of the media content that is associated with the next individual. The process media module 115 then processes the portion of the media content as just described to identify and document occurrences of the subject matter found in the portion of the media content. Once the process media module 115 processes the portion of the media content for each of the individuals involved in the media content, the process media module 115 exits in Operation 330.

As a result, an identifier is generated to document each occurrence of the subject matter found in the media content. In addition, each identifier may indicate the individual associated with the occurrence of the subject matter. Further, each identifier may indicate the type of subject matter that is associated with the occurrence. Accordingly, metadata can then be generated to include the identifiers and used in selectively redacting the subject matter from the media content. For example, the metadata can be used to redact certain types of subject matter from the media content. As another example, the metadata can be used to redact subject matter for certain individuals.

Occurrence Module

Turning now to FIG. 4, additional details are provided regarding an occurrence module 120 used for identifying and documenting occurrences of certain subject matter found in media content in accordance with various aspects. Accordingly, the flow diagram shown in FIG. 4 may correspond to operations carried out, for example, by computing hardware found in the selective redaction computing system 100 as described herein, as the computing hardware executes the occurrence module 120. The occurrence module 120 is discussed herein in terms of processing audio content and video content. But the occurrence module 120 may be used in processing other forms of media content in a similar manner as audio content and/or video content.

The process 400 involves the occurrence module 120 separating the media content into content segments in Operation 410. For example, the media content may involve audio content. Therefore, the occurrence module 120 may separate the audio content into a sequence of audio content segments representing an order in which the audio is heard in the audio content (e.g., representing an order in which the words are spoken and heard in the audio content).

In some aspects, the occurrence module 120 may generate the sequence of audio content segments based on different parameters. For example, the occurrence module 120 may generate the sequence of audio content segments based on each audio content segment representing an amount of time (e.g., a duration of time) or amount of audio data for the audio content. The occurrence module 120 may use information found in metadata provided for the audio content in setting the amount of time or the amount of audio data to use in generating the sequence of audio content segments.

As a specific example, the metadata may indicate the runtime for the audio content is thirty minutes. Therefore, the occurrence module 120 may generate the sequence of audio content segments based on the runtime by dividing the audio content into ten audio content segments with each audio content segment being three minutes of audio. As another specific example, the metadata may indicate that the bit rate is ten megabits per minute and the runtime for the audio content is thirty minutes. Therefore, the occurrence module 120 may generate the sequence of audio content segments based on the bit rate and runtime by dividing the audio content into ten audio content segments with each audio content segment having thirty megabits. Accordingly, the occurrence module 120 may be configured to execute an algorithm that uses the runtime, bit rate, and/or the like in determine the size for each audio content segment.

In another example, the occurrence module 120 may generate the sequence of audio content segments based on each audio content segment representing an utterance spoken in the audio content. An utterance is a unit of speech. Specifically, an utterance can be considered a stretch of spoken language (spoken words) that is preceded by silence and followed by silence, or a change of speaker. For example, a group of utterances can represent exchanges between individuals during a conversation (e.g., dialogue). In some aspects, the occurrence module 120 may use an audio editing software application (e.g., a speech analytics application) in identifying utterances in the audio content. Here, the occurrence module 120 can generate the sequence of audio content segments based on the utterances with each audio content segment found in the sequence representing a single utterance by an individual in the audio content.

With respect to video content, the occurrence module 120 may generate a sequence of video content segments representing an order in which video images are shown in the video content. For example, the occurrence module 120 may generate the sequence of video content segments based on each video content segment representing an amount of time (e.g., a duration of time), a number of frames, or an amount of video data for the video content. The occurrence module 120 may use information found in metadata provided for the video content in setting the amount of time, number of frames, or amount of video data to use in generating the sequence of video content segments.

As a specific example, the metadata may indicate the runtime for the video content is sixty minutes. Therefore, the occurrence module 120 may generate the sequence of video content segments based on the runtime by dividing the video content into sixty video content segments with each video content segment being one minute of video. As another specific example, the metadata may indicate that the frame rate is fifteen frames per second and the runtime for the video content is ten minutes. Therefore, the occurrence module 120 may generate the sequence of video content segments based on the frame rate and runtime by dividing the video content into ninety video content segments with each video content segment having one-hundred frames. Accordingly, the occurrence module 120 may be configured to execute an algorithm that uses the runtime, frame rate, and/or the like in determine the size for each video content segment.

Once the occurrence module 120 has generated the sequence of content segments, the occurrence module 120 selects a first content segment in Operation 415. The occurrence module then preprocesses the content segment in Operation 420. For example, if the content segment is an audio content segment, then the occurrence module 120 may process the audio content segment using a speech-to-text software application to generate a text representation of the audio content segment. Accordingly, the text representation may provide a sequence of words spoken in the audio content segment.

However, if the content segment is a video content segment, then the occurrence module 120 may process the video content segment by resizing the video content segment to a fixed size. For instance, the occurrence module 120 may have to resize the video content segment to a fixed size due to how the video content was segmented into the sequence of video content segments. For example, the occurrence module 120 may have segmented the video content based on a duration of time. As a result, some of the video content segments may be larger than other video content segments (e.g., some video content segments may involve more video data than other video content segments). Therefore, the occurrence module 120 may resize the video content segment to a fixed size so that the video content segment is better formatted for further processing.

At operation 425, the occurrence module 120 classifies the content segment using a multilabel classification machine-learning model to identify occurrences of certain subject matter in the content segment. For example, if the content segment is an audio content segment, then the occurrence module 120 may process the text representation of the audio content segment using the multilabel classification machine-learning model to generate a feature representation (e.g., a feature vector) having components representing different keywords of interest that may be present (e.g., spoken) in the audio content segment. Accordingly, each component of the feature representation may provide a prediction (e.g., a prediction value) as to whether the corresponding keyword is found in the audio content segment.

A keyword may be a single word, a phrase, a group of words, and/or the like. Here, a keyword represents an occurrence of one or more spoken words that the entity may wish to have identified and documented for the audio content segment. For example, a keyword may represent inappropriate content that the entity wishes to have identified and documented for the audio content segment such as a curse word. In another example, a keyword may represent personal and/or sensitive data that the entity wishes to have identified and documented for the audio content segment such as an individual's phone number, social security number, credit card number, and/or the like. Yet, in another example, a keyword may represent sensitive content that the entity wishes to have identified and documented from the audio content segment such as competitor names, advertising, trade secret references, and/or the like.

The multilabel classification machine-learning model may be an ensemble of multiple classifiers in which each classifier is used in generating a prediction as to whether a particular keyword is present in the audio content segment. In some aspects, the selective redaction computing system 100 may provide an entity (e.g., personnel of the entity) with a number of different classifiers to choose from in processing the audio content segments. Personnel for the entity may choose which classifiers are of interest to use in identifying and documenting occurrences of subject matter for the audio media content. For example, the personnel may select a first classifier used in generating a prediction for a first type of personal data such as a first and last name, a second classifier used in generating a prediction for a second type of personal data such as an email address, a third classifier used in generating a prediction for a third type of personal data such as a phone number, and so forth. In some instances, the classifiers may be provided in sets that can be selected by the personnel. For example, the personnel may be provided the option of selecting a first set of classifiers for certain inappropriate content, a second set of classifiers for certain personal data, a third set of classifiers for certain sensitive content, and so forth.

In other aspects, the selective redaction computing system 100 (e.g., the documentation module 110) may automatically select the classifiers to be used by the multilabel classification machine-learning model. For example, the selective redaction computing system 100 may determine a context associated with the media content and based on the context, identify which of the classifiers should be used by the multilabel classification machine-learning model.

In some aspects, the multilabel classification machine-learning model is a convolutional neural network for classifying objects in video content segments. Here, the convolutional neural network can be used to identify various objects that may be shown in the video content segment and are associated with certain subject matter. For instance, the video content segment may show various objects associated with certain subject matter that the entity may wish to have identified and documented for the video content segment. For example, such objects may involve individuals visible in the video content. In another example, such objects may involve buildings, signs, walls, and/or the like visible in the video content that are displaying inappropriate, personal, and/or sensitive information.

According to particular aspects, the multilabel classification machine-learning model used for processing video content segments generates a feature representation (e.g., a feature vector) having components representing different objects that may be present in the video content segment and associated with certain subject matter. Each component may provide a prediction (e.g., a prediction value) as to whether the associated object is present in the video content segment. Therefore, similar to the multilabel classification machine-learning model used in processing audio content segments, the multilabel classification machine-learning model used in processing video content segments may be an ensemble of classifiers in which each classifier is used in generating a prediction as to whether a particular object associated with certain subject matter is present in the video content segment.

The occurrence module 120 may use multiple multilabel classification machine-learning models for processing content segments for different forms of content. For example, the occurrence module 120 may use a first multilabel classification machine-learning model in processing audio content segments and a second, different multilabel classification machine-learning model in processing video content segments.

Therefore, in some aspects, the occurrence module 120 determines a particular item (e.g., a particular keyword or object) is present in the content segment if the item's corresponding prediction satisfies a threshold. For example, the occurrence module 120 may determine a particular keyword in present in an audio content segment if the multilabel classification machine-learning model generates a prediction value equal to or greater than sixty percent. Different thresholds may be used for different types of subject matter, different items associated with certain subject matter, and/or different forms of media content. Thus, the occurrence module 120 determines whether an occurrence of the subject matter is found in the content segment and therefore, the content segment needs to be documented in Operation 430. If so, then the occurrence module 120 documents the occurrence of the subject matter for content segment in Operation 435.

In some aspects, the occurrence module 120 documents the occurrence of the subject matter for the content segment by generating an identifier for the content segment. The identifier identifies the content segment as containing the item associated with the subject matter. In addition, the identifier may identify a type of subject matter associated with the item. For example, the identifier may identify that the item contains personal data that is a telephone number for an individual. Further, the identifier may identify an individual associated with the subject matter.

At Operation 440, the occurrence module 120 determines whether the media content being analyzed involves another segment of content (e.g., determines whether another content segment is found in the sequence of content segments). If so, then the occurrence module 120 returns to Operation 415, selects the next content segment, and processes the newly selected content segment to identify and document the subject matter for the segment, if present, as just described. Once the occurrence module 120 has analyzed all of the content segments for the media content, the occurrence module 120 exists in Operation 445.

Redact Media Module

Turning now to FIG. 5, additional details are provided regarding a redact media module 125 used for processing media content to redact certain subject matter found in the media content in accordance with various aspects. Accordingly, the flow diagram shown in FIG. 5 may correspond to operations carried out, for example, by computing hardware found in the selective redaction computing system 100 as described herein, as the computing hardware executes the redact media module 125.

Personnel for an entity interested in having media content processed to redact certain subject matter found in the media content may access a selective redaction service provided through a selective redaction computing system 100. For example, the personnel may access the service through a website published by the selective redaction computing system 100 over one or more networks 150 (such as the Internet). The web site may provide the personnel with one or more webpages (e.g., graphical user interfaces) to facilitate conducting a redaction process on the media content. The personnel may provide the media content that is to be redacted directly to the selective redaction computing system 100 or may provide a location of the media content stored on a media source 170 that the selective redaction computing system 100 then accesses. In addition, the personnel may submit a request through the service to have the media content redacted.

Therefore, the process 500 involves the redact media module 125 receiving the request for redacting the media content in Operation 510. The request may include various information for the redact media module 125 to use in redacting the media content. For example, the request may indicate a certain type of subject matter to redact from the media content. As a specific example, the request may indicate to redact occurrences of medical history referenced in the media content. In addition, the request may indicate a particular individual who is involved in the media content and whose occurrences of the subject matter should be redacted from the media content. Further, the request may indicate a particular form of the media content that is to be redacted. For example, the request the audio content, video content, or both of the media content should be redacted.

At Operation 515, the redact media module 125 retrieves metadata for the media content. In some aspects, the personnel may provide the metadata along with the request to redact the media content. In other aspects, the redact media module 125 may retrieve the metadata from some type of repository 140 used by the selective redaction computing system 100. Accordingly, the metadata provides the identifiers for the different occurrences of the subject matter identified for the media content. Each identifier may indicate the type of subject matter that is associated with each occurrence, as well as the individual associated with the occurrence. In addition, the metadata may provide information on the process used for generating the sequence of media content segments for each of the forms of media that make up the media content.

At Operation 520, the redact media module 125 separates the media content into its individual forms. For instance, the redact media module 125 may separate the video content from the audio content that make up the media content. According to particular aspects, the redact media module 125 may use a media content editor software application to extract the audio and/or video content from the media content. For example, the media content may be provided in a file format that is a container having video data in a video coding format and audio data in an audio coding format. Therefore, the redact media module 125 may use the media content editor in separating out the video data and the audio data from the container. In addition, the container may include metadata providing information on the media content that may be helpful in performing this operation such as, for example, frame count, frame rate, channels for the audio, sampling rate, duration, and/or the like.

At Operation 525, the redact media module 125 selects a form of media content provided in the media content. For example, the redact media module 125 selects the audio content for the media content or the video content for the media content. If appropriate, the redact media module 125 selects the portion of the form of media content associated with a particular individual in Operation 530. The redact media module 125 then processes the form of media content (portion thereof) in Operation 535 to redact occurrences of the subject matter found in the media content based on the request to generate a redacted form of media content. For example, the redact media module 125 may process the audio content or the video content to redact occurrences of the subject matter for a certain type of the subject matter and/or for a certain individual as indicated in the request.

In some aspects, the redact media module 125 performs this particular operation by invoking a redaction module 130. As detailed further herein, the redaction module 130 segments the form of media content into media content segments. The redaction module 130 then redacts the subject matter from the appropriate media content segments based on the information provided in the request to generate redacted media content segments. Once the redaction module 130 has redacted the subject matter from the appropriate media content segments, the redaction module 130 reassembles the media content segments, using the redacted audio content segments where appropriate, to generate the redacted form of media content.

At Operation 540, the redact media module 125 determines whether the form of media content needs to be redacted for another individual. If so, then the redact media module 125 returns to Operation 530, selects the portion of the form of the media content for the next individual, and redacts the subject matter from the portion as just described. Once the redact media module 125 has processed the form of media content for all the needed individuals, the redact media module 125 determines whether another form of media content needs to be redacted in Operation 545. If so, then the redact media module 125 returns to Operation 525, selects the next form of media contents, and processes the form of media content as just described.

Once the redact media module 125 has processed the appropriate forms of media content, the redact media module 125 recombines the redacted forms of media content generated for the media content to generate redacted media content in Operation 550. In some aspects, the redact media module 125 uses the media content editor to recombine the redacted forms of media content into the redacted media content. For example, the metadata provided in the container for the original media content may include synchronization information that may be used in combining (e.g., synchronizing) the redacted audio content with the redacted video content. The redact media module 125 may use the media content editor and synchronization information to recombine the container for the media content with the redacted audio content and redacted video content to generate the redacted media content.

Redaction Module

Turning now to FIG. 6, additional details are provided regarding a redaction module 130 used for redacting certain subject matter found in media content in accordance with various aspects. Accordingly, the flow diagram shown in FIG. 6 may correspond to operations carried out, for example, by computing hardware found in the selective redaction computing system 100 as described herein, as the computing hardware executes the redaction module 130. The redaction module 130 is discussed herein in terms of redacting audio content and video content. But the redaction module 130 may be used in redacting other forms of media content in a similar manner as audio content and/or video content.

The process 600 involves the redaction module 130 separating the media content into content segments in Operation 610. In some aspects, the redaction module 130 performs this operation in a similar fashion as previously discussed with respect to the occurrence module 120. For example, the metadata generated for the media content may provide information on the process using in generating the sequence of content segments during the documentation process. Therefore, the redaction module 130 may use this information in generating the same or similar sequence of content segments as the sequence of content segments used during the documentation process.

Once the redaction module 130 has generated the sequence of content segments, the redaction module 130 selects a first content segment in Operation 615. The redaction module 130 then references the metadata generated for the media content with respect to the selected content segment in Operation 620. For example, the redaction module 130 determines whether an identifier is provided in the metadata for the content segment indicating the content segment contains an item associated with the subject matter.

At Operation 625, the redaction module 130 determines whether the content segment needs to be redacted. For example, the redaction module 130 may determine the content segment needs to be redacted based on the identifier being present in the metadata. In some instances, the redaction module 130 may be instructed to perform a selective redaction on the content media with respect to a certain type of subject matter. Therefore, the redaction module 130 may determine whether the identifier indicates the data subject found in the content segment is of the type that needs to be redacted. If the redaction module 130 determines the content segment needs to be redacted, then the redaction module 130 redacts the content segment in Operation 630 to generate a redacted content segment.

The redaction module 130 may redact the content segment using various techniques to generate the redacted content segment. For instance, for an audio content segment, the redaction module 130 may redact one or more words spoken in the audio content segment that are related to the subject matter. The redaction may involve removing the one or more words associated with the subject matter entirely from the audio content segment, replacing the one or more words associated with the certain subject matter in the audio content segment with one or more words that are not directed to the subject matter, altering the audio content segment so that the one or more words directed to the subject matter are distorted, and/or replacing the entire audio content segment with a replacement audio content segment that has no words that are directed to the subject matter.

For a video content segment, the redaction module 130 may redact one or more objects shown in the video content segment that are related to the subject matter. For example, the redaction may involve removing the one or more objects associated with the subject matter entirely from the video content segment, replacing one or more frames containing the one or more objects found in the segment with frame(s) having a blank or distorted image, altering the video content segment so that the one or more objects associated with the subject matter are not recognizable such as partially distorting the one or more objects associated with the subject matter so that they are no longer visible, and/or replacing the entire video content segment with a replacement video content segment that does not display the one or more objects associated with the subject matter.

In some aspects, the redaction module 130 may use a rules-based model in determining what to redact from the content segment, as well as how to redact the content segment, to generate the redacted content segment. The redaction module 130 may process the one or more items (e.g., the one or more keywords or objects) identified for the content segment as being associated with certain subject matter using the rules-based model to generate a redaction action for the redaction module 130 to perform on the content segment. Accordingly, the rules-based model may use one or more sets of rules in generating the redaction action. In some aspects, the rules-based model may use a particular set of rules based on the type of content segment being redacted (e.g., an audio content segment or video content segment), as well as the subject matter that is being redacted from the content segment.

For example, the redaction module 130 may have identified that a keyword is present in an audio content segment (e.g., “social security number”) indicating an individual's social security number is provided (e.g., spoken) in the audio content segment. Here, the rules-based model may generate a redaction action that involves replacing the numbers representing the individual's social security number with zeros in the audio content segment. Therefore, the redaction module 130 may identify the location of the numbers in the audio content segment based on a text representation generated for the audio content segment and then process the audio content segment using a media content editor to replace the numbers with digitally generated audio of the word “zero” for each of the numbers to generate the redacted audio content segment with the numbers masked by zeros.

As another example, the redaction module 130 may have identified an object represent a sign displaying subject matter that need to be redacted. Here, the rules-based model may generate a redaction action that involves altering the image of the sign in the video content segment so that the image of the sign is blurred. Therefore, the redaction module 130 may replace one or more frames of the video content segment with frame(s) having a blurred image to generate a redacted video content segment with the sign blurred.

In some instances, the multilabel classification machine-learning model used for processing video content segments may be a region-based convolutional neural network and may generate a bounding box, along with a prediction for the particular object, which provides a location of the object in the video content segment (e.g., in one or more frames thereof). For example, the bounding box may be defined by a point, width, and height position for the video content segment (e.g., one or more frames thereof). Therefore, the redaction module 130 may replace the pixels of the video content segment that are associated with the sign found within the bounding box with blurred pixels (via a media content editor) to generate a redacted video content segment with the individual firing the weapon blurred in the redacted video content segment.

At Operation 635, the redaction module 130 determines whether the media content being redacted involves another segment of content (e.g., determines whether another content segment in found in the sequence of content segments). If so, then the redaction module 130 returns to Operation 615, selects the next content segment, and processes the newly selected content segment to redact the subject matter from the segment, if required, as just described.

Once the redaction module 130 has processed the content segments, the redaction module 130 reassembles the content segments for the media content in Operation 640 to generate redacted media content. For example, the redaction module 130 may reassemble the content segments based on the order identified for the sequence of content segments. Accordingly, the redaction module 130 reassembles the content segments such that any of the content segments found to have occurrences of the subject matter that needed to be redacted (e.g., found to have occurrences of keywords or object associated with the subject matter that needed to be redacted) are replaced with their corresponding redacted content segments and as a result, generates the redacted media content. The redacted media content (form thereof) can then be recombined with other (redacted) forms of media content to generate redacted media content comprising the forms of redacted media content.

Example Technical Platforms

Aspects of the present disclosure may be implemented in various ways, including as computer program products that comprise articles of manufacture. Such computer program products may include one or more software components including, for example, software objects, methods, data structures, and/or the like. A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and/or operating system platform. A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform. Another example programming language may be a higher-level programming language that may be portable across multiple architectures. A software component comprising higher-level programming language instructions may require conversion to an intermediate representation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to, a macro language, a shell or command language, a job control language, a script language, a database query, or search language, and/or a report writing language. In one or more example aspects, a software component comprising instructions in one of the foregoing examples of programming languages may be executed directly by an operating system or other software component without having to be first transformed into another form. A software component may be stored as a file or other data storage construct. Software components of a similar type or functionally related may be stored together such as, for example, in a particular directory, folder, or library. Software components may be static (e.g., pre-established, or fixed) or dynamic (e.g., created or modified at the time of execution).

A computer program product may include a non-transitory computer-readable storage medium storing applications, programs, program modules, scripts, source code, program code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like (also referred to herein as executable instructions, instructions for execution, computer program products, program code, and/or similar terms used herein interchangeably). Such non-transitory computer-readable storage media include all computer-readable media (including volatile and non-volatile media).

In some aspects, a non-volatile computer-readable storage medium may include a floppy disk, flexible disk, hard disk, solid-state storage (SSS) (e.g., a solid-state drive (SSD), solid state card (SSC), solid state module (SSM)), enterprise flash drive, magnetic tape, or any other non-transitory magnetic medium, and/or the like. A non-volatile computer-readable storage medium may also include a punch card, paper tape, optical mark sheet (or any other physical medium with patterns of holes or other optically recognizable indicia), compact disc read only memory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc (DVD), Blu-ray disc (BD), any other non-transitory optical medium, and/or the like. Such a non-volatile computer-readable storage medium may also include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC), secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF) cards, Memory Sticks, and/or the like. Further, a non-volatile computer-readable storage medium may also include conductive-bridging random access memory (CBRAM), phase-change random access memory (PRAM), ferroelectric random-access memory (FeRAM), non-volatile random-access memory (NVRAM), magnetoresistive random-access memory (MRAM), resistive random-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory (SONOS), floating junction gate random access memory (FJG RAM), Millipede memory, racetrack memory, and/or the like.

In some aspects, a volatile computer-readable storage medium may include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), fast page mode dynamic random access memory (FPM DRAM), extended data-out dynamic random access memory (EDO DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), double data rate type two synchronous dynamic random access memory (DDR2 SDRAM), double data rate type three synchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), Twin Transistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM), Rambus in-line memory module (RIMM), dual in-line memory module (DIMM), single in-line memory module (SIMM), video random access memory (VRAM), cache memory (including various levels), flash memory, register memory, and/or the like. It will be appreciated that where various aspects are described to use a computer-readable storage medium, other types of computer-readable storage media may be substituted for or used in addition to the computer-readable storage media described above.

Various aspects of the present disclosure may also be implemented as methods, apparatuses, systems, computing devices, computing entities, and/or the like. As such, various aspects of the present disclosure may take the form of a data structure, apparatus, system, computing device, computing entity, and/or the like executing instructions stored on a computer-readable storage medium to perform certain steps or operations. Thus, various aspects of the present disclosure also may take the form of entirely hardware, entirely computer program product, and/or a combination of computer program product and hardware performing certain steps or operations.

Various aspects of the present disclosure are described below with reference to block diagrams and flowchart illustrations. Thus, each block of the block diagrams and flowchart illustrations may be implemented in the form of a computer program product, an entirely hardware aspect, a combination of hardware and computer program products, and/or apparatuses, systems, computing devices, computing entities, and/or the like carrying out instructions, operations, steps, and similar words used interchangeably (e.g., the executable instructions, instructions for execution, program code, and/or the like) on a computer-readable storage medium for execution. For example, retrieval, loading, and execution of code may be performed sequentially such that one instruction is retrieved, loaded, and executed at a time. In some examples of aspects, retrieval, loading, and/or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and/or executed together. Thus, such aspects can produce specially configured machines performing the steps or operations specified in the block diagrams and flowchart illustrations. Accordingly, the block diagrams and flowchart illustrations support various combinations of aspects for performing the specified instructions, operations, or steps.

Example System Architecture

FIG. 7 is a block diagram of a system architecture 700 that can be used in identifying and documenting certain subject matter from media content, as well as redacting the subject matter from the media content in some aspects as detailed herein. Components of the system architecture 700 are configured in some aspects to assist an entity in documenting and/or redacting certain subject matter found in media content that may be stored, for example, locally on a selective redaction computing system 100 in a repository 140 or remotely in a media source 170 that is accessible over one or more networks 150.

As may be understood from FIG. 7, the system architecture 700 in some aspects may include the selective redaction computing system 100 that comprises one or more selective redaction servers 710 and one or more data repositories 140. For example, the one or more data repositories 140 may include a data repository for storing media content that is to be documented and/or redacted. In addition, the one or more data repositories 140 may include a repository for storing one or more multilabel classification machine-learning models and/or classifiers for use with the model(s) as detailed herein. Further, the one or more data repositories 140 may include a repository for storing a rules-based model and/or various sets of rules that can be used by the model in generating redaction actions as detailed herein. Although the selective redaction server(s) 710 and repository(ies) 140 are shown as separate components, according to other aspects, these components 710, 140 may comprise a single server and/or repository, a plurality of servers and/or repositories, one or more cloud-based servers and/or repositories, or any other suitable configuration.

As previously noted, the selective redaction computing system 100 may provide a selective redaction service to various entities that is available over one or more networks 150. Here, an entity may access the service via a client computing system 160 associated with the entity. For example, the selective redaction computing system 100 may provide the service through a website that is accessible to the entity's client computing system 160 over the one or more networks 150.

According, the selective redaction server(s) 710 may execute a documentation module 110, a process media module 115, an occurrence module 120, a redact media module 125, and a redaction module 130 as described herein. Further, according to particular aspects, the selective redaction server(s) 710 may provide one or more graphical user interfaces (e.g., one or more webpages through a web site) through which personnel of an entity can interact with the selective redaction computing system 100. Furthermore, the selective redaction server(s) 710 may provide one or more interfaces that allow the selective redaction computing system 100 to communicate with client computing system(s) 160 and/or media source(s) 170 such as one or more suitable application programming interfaces (APIs), direct connections, and/or the like.

Example Computing Hardware

FIG. 8 illustrates a diagrammatic representation of a computing hardware device 800 that may be used in accordance with various aspects. For example, the hardware device 800 may be computing hardware such as a selective redaction server 710 as described in FIG. 7. According to particular aspects, the hardware device 800 may be connected (e.g., networked) to one or more other computing entities, storage devices, and/or the like via one or more networks such as, for example, a LAN, an intranet, an extranet, and/or the Internet. As noted above, the hardware device 800 may operate in the capacity of a server and/or a client device in a client-server network environment, or as a peer computing device in a peer-to-peer (or distributed) network environment. In some aspects, the hardware device 800 may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a mobile device (smartphone), a web appliance, a server, a network router, a switch or bridge, or any other device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single hardware device 800 is illustrated, the term “hardware device,” “computing hardware,” and/or the like shall also be taken to include any collection of computing entities that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

A hardware device 800 includes a processor 802, a main memory 804 (e.g., read-only memory (ROM), flash memory, dynamic random-access memory (DRAM) such as synchronous DRAM (SDRAM), Rambus DRAM (RDRAM), and/or the like), a static memory 806 (e.g., flash memory, static random-access memory (SRAM), and/or the like), and a data storage device 818, that communicate with each other via a bus 832.

The processor 802 may represent one or more general-purpose processing devices such as a microprocessor, a central processing unit, and/or the like. According to some aspects, the processor 802 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, processors implementing a combination of instruction sets, and/or the like. According to some aspects, the processor 802 may be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, and/or the like. The processor 802 can execute processing logic 826 for performing various operations and/or steps described herein.

The hardware device 800 may further include a network interface device 808, as well as a video display unit 810 (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), and/or the like), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse, a trackpad), and/or a signal generation device 816 (e.g., a speaker). The hardware device 800 may further include a data storage device 818. The data storage device 818 may include a non-transitory computer-readable storage medium 830 (also known as a non-transitory computer-readable storage medium or a non-transitory computer-readable medium) on which is stored one or more modules 822 (e.g., sets of software instructions) embodying any one or more of the methodologies or functions described herein. For instance, according to particular aspects, the modules 822 include a documentation module 110, process media module 115, occurrence module 120, redact media module 125, and/or redaction module 130 as described herein. The one or more modules 822 may also reside, completely or at least partially, within main memory 804 and/or within the processor 802 during execution thereof by the hardware device 800—main memory 804 and processor 802 also constituting computer-accessible storage media. The one or more modules 822 may further be transmitted or received over a network 150 via the network interface device 808.

While the computer-readable storage medium 830 is shown to be a single medium, the terms “computer-readable storage medium” and “machine-accessible storage medium” should be understood to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” should also be understood to include any medium that is capable of storing, encoding, and/or carrying a set of instructions for execution by the hardware device 800 and that causes the hardware device 800 to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” should accordingly be understood to include, but not be limited to, solid-state memories, optical and magnetic media, and/or the like.

System Operation

The logical operations described herein may be implemented (1) as a sequence of computer implemented acts or one or more program modules running on a computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance and other requirements of the computing system. Accordingly, the logical operations described herein are referred to variously as states, operations, steps, structural devices, acts, or modules. These states, operations, steps, structural devices, acts, and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof. Greater or fewer operations may be performed than shown in the figures and described herein. These operations also may be performed in a different order than those described herein.

CONCLUSION

While this specification contains many specific aspect details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features that may be specific to particular aspects of particular inventions. Certain features that are described in this specification in the context of separate aspects also may be implemented in combination in a single aspect. Conversely, various features that are described in the context of a single aspect also may be implemented in multiple aspects separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be a sub-combination or variation of a sub-combination.

Similarly, while operations are described in a particular order, this should not be understood as requiring that such operations be performed in the particular order described or in sequential order, or that all described operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various components in the various aspects described above should not be understood as requiring such separation in all aspects, and the described program components (e.g., modules) and systems may be integrated together in a single software product or packaged into multiple software products.

Many modifications and other aspects of the disclosure will come to mind to one skilled in the art to which this disclosure pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosure is not to be limited to the specific aspects disclosed and that modifications and other aspects are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for the purposes of limitation. 

1. A method comprising: obtaining, by computing hardware, first metadata for media content comprising a first portion of audio content of a first individual involved in the media content and a second portion of audio content of a second individual involved in the media content, wherein the first metadata identifies (1) a context for the media content, (2) the first portion of audio content is associated with the first individual, and (3) the second portion of audio content is associated with the second individual; identifying, by the computing hardware and based on the context, a certain subject matter for the media content; for the first portion of audio content: segmenting, by the computing hardware, the first portion of audio content into a first sequence of audio content segments; processing, by the computing hardware, each audio content segment of the first sequence of audio content segments using a multilabel classification machine-learning model to generate a first feature representation comprising a plurality of components for the audio content segment, wherein each component of the plurality of components represents a particular keyword associated with the subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, by the computing hardware and based on the prediction provided for a particular component of the plurality of components generated for a first particular audio content segment of the first sequence of audio content segments, that the particular keyword associated with the subject matter is present in the first particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the first particular audio content segment, generating, by the computing hardware, a first identifier for the first particular audio content segment that identifies the particular keyword associated with the subject matter is present in the first particular audio content segment; for the second portion of audio content: segmenting, by the computing hardware, the second portion of audio content into a second sequence of audio content segments; processing, by the computing hardware, each audio content segment of the second sequence of audio content segments using the multilabel classification machine-learning model to generate a second feature representation comprising the plurality of components for the audio content segment, wherein each component of the plurality of components represents the particular keyword associated with the subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, by the computing hardware and based on the prediction provided for a particular component of the plurality of components generated for a second particular audio content segment of the second sequence of audio content segments, that the particular keyword associated with the subject matter is present in the second particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the second particular audio content segment, generating, by the computing hardware, a second identifier for the second particular audio content segment that identifies the particular keyword associated with the subject matter is present in the second particular audio content segment; and generating, by the computing hardware, second metadata for the media content, wherein the second metadata comprises the first identifier and the second identifier.
 2. The method of claim 1 further comprising: receiving a first request for the media content, wherein the first request identifies the first individual; identifying, by the computing hardware based on the first request identifying the first individual, the first identifier in the second metadata for the media content; segmenting, by the computing hardware, the first portion of audio content into the first sequence of audio content segments; generating, by the computing hardware based on the first identifier, a first redacted audio content segment for the first particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the first particular audio content segment is absent from the first redacted audio content segment; reassembling, by the computing hardware, the first sequence of audio content segments with the first particular audio content segment replaced with the first redacted audio content segment to generate a first portion of redacted audio content for the media content; and responsive to the first request, providing, by the computing hardware, the media content comprising the first portion of redacted audio content, wherein playing the media content comprising the first portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment not being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment being heard spoken by the second individual.
 3. The method of claim 2 further comprising identifying, by the computing hardware and based on the first metadata and the first request identifying the first individual, the first portion of audio content of the media content.
 4. The method of claim 2, wherein generating the first redacted audio content segment comprises at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment.
 5. The method of claim 2, wherein generating the first redacted audio content segment comprises: processing the first particular audio content segment using a rules-based model to generate a redaction action, the redaction action involving at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment; and performing the redaction action to generate the first redacted audio content segment.
 6. The method of claim 2 further comprising: receiving a second request for the media content, wherein the second request identifies the second individual; identifying, by the computing hardware based on the second request identifying the second individual, the second identifier in the second metadata for the media content; segmenting, by the computing hardware, the second portion of audio content into the second sequence of audio content segments; generating, by the computing hardware based on the second identifier, a second redacted audio content segment for the second particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the second particular audio content segment is absent from the second redacted audio content segment; reassembling, by the computing hardware, the second sequence of audio content segments with the second particular audio content segment replaced with the second redacted audio content segment to generate a second portion of redacted audio content for the media content; and responsive to the second request, providing, by the computing hardware, the media content comprising the second portion of redacted audio content, wherein playing the media content comprising the second portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment not being heard spoken by the second individual.
 7. The method of claim 1, wherein processing each audio content segment of the first sequence of audio content segments using the multilabel classification machine-learning model to generate the first feature representation comprises: processing the audio content segment using a speech-to-text engine to generate a text representation of the audio content segment, the text representation comprising a sequence of words spoken in the audio content segment; and processing the text representation using the multilabel classification machine-learning model to generate the first feature representation.
 8. A system comprising: a non-transitory computer-readable medium storing instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium, wherein, the processing device is configured to execute the instructions and thereby perform operations comprising: obtaining first metadata for media content comprising a first portion of video content associated with a first individual involved in the media content and a second portion of video content associated with a second individual involved in the media content, wherein the first metadata identifies (1) a context for the media content, (2) the first portion of video content is associated with the first individual, and (3) the second portion of video content is associated with the second individual; identifying, based on the context, a certain subject matter for the media content; for the first portion of video content: segmenting the first portion of video content into a first sequence of video content segments; processing each video content segment of the first sequence of video content segments using a multilabel classification machine-learning model to generate a first feature representation comprising a plurality of components for the video content segment, wherein each component of the plurality of components represents a particular object associated with the subject matter and provides a prediction as to whether the particular object associated with the subject matter is present in the video content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a first particular video content segment of the first sequence of video content segments, that the particular object associated with the subject matter is present in the first particular video content segment; and responsive to determining that the particular object associated with the subject matter is present in the first particular video content segment, generating a first identifier for the first particular video content segment that identifies the particular object associated with the subject matter is present in the first particular video content segment; for the second portion of video content: segmenting the second portion of video content into a second sequence of video content segments; processing each video content segment of the second sequence of video content segments using the multilabel classification machine-learning model to generate a second feature representation comprising the plurality of components for the video content segment, wherein each component of the plurality of components represents the particular object associated with the subject matter and provides a prediction as to whether the particular object associated with the subject matter is present in the video content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a second particular video content segment of the second sequence of video content segments, that the particular object associated with the subject matter is present in the second particular video content segment; and responsive to determining that the particular object associated with the subject matter is present in the second particular video content segment, generating a second identifier for the second particular video content segment that identifies the particular object associated with the subject matter is present in the second particular video content segment; and generating second metadata for the media content, wherein the second metadata comprises the first identifier and the second identifier.
 9. The system of claim 8, wherein the operations further comprise: receiving a first request for the media content, wherein the first request identifies the first individual; identifying, based on the first request identifying the first individual, the first identifier in the second metadata for the media content; segmenting the first portion of video content into the first sequence of video content segments; generating, based on the first identifier, a first redacted video content segment for the first particular video content segment, wherein the particular object associated with the subject matter that is present in the first particular video content segment is absent from the first redacted video content segment; reassembling the first sequence of video content segments with the first particular video content segment replaced with the first redacted video content segment to generate a first portion of redacted video content for the media content; and responsive to the first request, providing the media content comprising the first portion of redacted video content, wherein playing the media content comprising the first portion of redacted video content results in the particular object associated with the subject matter that is present in the first particular video content segment not being displayed and the particular object associated with the subject matter that is present in the second particular video content segment being displayed.
 10. The system of claim 9, wherein generating the first redacted video content segment comprises at least one of removing the particular object associated with the subject matter from the first particular video content segment, replacing the particular object associated with the subject matter in the first particular video content segment, or altering the particular object associated with the subject matter in the first particular video content segment.
 11. The system of claim 9, wherein the first feature representation comprises a bounding box defined by a point, width, and height position for the particular object associated with the subject matter in the first particular video content segment and generating the first redacted video content segment comprises: performing, based on the particular object being located within the bounding box, at least one of removing the particular object associated with the subject matter from the first particular video content segment, replacing the particular object associated with the subject matter in the first particular video content segment, or altering the particular object associated with the subject matter in the first particular video content segment.
 12. The system of claim 9, wherein generating the first redacted video content segment comprises replacing one or more frames of the first particular video content segment comprising the particular object associated with the subject matter with one or more replacement frames comprising altered content.
 13. The system of claim 9, wherein the operations further comprise: receiving a second request for the media content, wherein the second request identifies the second individual; identifying, based on the second request identifying the second individual, the second identifier in the second metadata for the media content; segmenting the second portion of video content into the second sequence of video content segments; generating, based on the second identifier, a second redacted video content segment for the second particular video content segment, wherein the particular object associated with the subject matter that is present in the second particular video content segment is absent from the second redacted video content segment; reassembling the second sequence of video content segments with the second particular video content segment replaced with the second redacted video content segment to generate a second portion of redacted video content for the media content; and responsive to the second request, providing the media content comprising the second portion of redacted video content, wherein playing the media content comprising the second portion of redacted video content results in the particular object associated with the subject matter that is present in the first particular video content segment being shown and the particular object associated with the subject matter that is present in the second particular video content segment not being shown.
 14. The system of claim 8, wherein determining that the particular object associated with the subject matter is present in the first particular video content segment is based on the prediction provided for the particular component satisfying a threshold.
 15. A non-transitory computer-readable medium having program code that is stored thereon, the program code executable by one or more processing devices for performing operations comprising: obtaining first metadata for media content comprising first portion of audio content of a first individual involved in the media content and second portion of audio content of a second individual involved in the media content, wherein the first metadata identifies (1) the first portion of audio content is associated with the first individual and (2) the second portion of audio content is associated with the second individual; for the first portion of audio content: segmenting the first portion of audio content into a first plurality of audio content segments; processing each audio content segment of the first plurality of audio content segments using a multilabel classification machine-learning model to generate a first feature representation comprising a plurality of components for the audio content segment, wherein each component of the plurality of components represents a particular keyword associated with certain subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a first particular audio content segment of the first plurality of audio content segments, that the particular keyword associated with the subject matter is present in the first particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the first particular audio content segment, generating a first identifier for the first particular audio content segment that identifies the particular keyword associated with the subject matter is present in the first particular audio content segment; for the second portion of audio content: segmenting the second portion of audio content into a second plurality of audio content segments; processing each audio content segment of the second plurality of audio content segments using the multilabel classification machine-learning model to generate a second feature representation comprising the plurality of components for the audio content segment, wherein each component of the plurality of components represents the particular keyword associated with the subject matter and provides a prediction as to whether the particular keyword associated with the subject matter is present in the audio content segment; determining, based on the prediction provided for a particular component of the plurality of components generated for a second particular audio content segment of the second plurality of audio content segments, that the particular keyword associated with the subject matter is present in the second particular audio content segment; and responsive to determining that the particular keyword associated with the subject matter is present in the second particular audio content segment, generating a second identifier for the second particular audio content segment that identifies the particular keyword associated with the subject matter is present in the second particular audio content segment; and generating second metadata for the media content, wherein the second metadata comprises the first identifier and the second identifier.
 16. The non-transitory computer-readable medium of claim 15, wherein the operations further comprise: receiving a first request for the media content, wherein the first request identifies the first individual; identifying, based on the first request identifying the first individual, the first identifier in the second metadata for the media content; segmenting the first portion of audio content into the first plurality of audio content segments; generating, based on the first identifier, a first redacted audio content segment for the first particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the first particular audio content segment is absent from the first redacted audio content segment; reassembling the first plurality of audio content segments with the first particular audio content segment replaced with the first redacted audio content segment to generate a first portion of redacted audio content for the media content; and responsive to the first request, providing the media content comprising the first portion of redacted audio content, wherein playing the media content comprising the first portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment not being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment being heard spoken by the second individual.
 17. The non-transitory computer-readable medium of claim 16, wherein generating the first redacted audio content segment comprises at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment.
 18. The non-transitory computer-readable medium of claim 16, wherein generating the first redacted audio content segment comprises: processing the first particular audio content segment using a rules-based model to generate a redaction action, the redaction action involving at least one of removing the particular keyword associated with the subject matter from the first particular audio content segment, replacing the particular keyword associated with the subject matter in the first particular audio content segment, or altering the particular keyword associated with the subject matter in the first particular audio content segment; and performing the redaction action to generate the first redacted audio content segment.
 19. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise: receiving a second request for the media content, wherein the second request identifies the second individual; identifying, based on the second request identifying the second individual, the second identifier in the second metadata for the media content; segmenting the second portion of audio content into the second plurality of audio content segments; generating, based on the second identifier, a second redacted audio content segment for the second particular audio content segment, wherein the particular keyword associated with the subject matter that is present in the second particular audio content segment is absent from the second redacted audio content segment; reassembling the second plurality of audio content segments with the second particular audio content segment replaced with the second redacted audio content segment to generate a second portion of redacted audio content for the media content; and responsive to the second request, providing the media content comprising the second portion of redacted audio content, wherein playing the media content comprising the second portion of redacted audio content results in the particular keyword associated with the subject matter that is present in the first particular audio content segment being heard spoken by the first individual and the particular keyword associated with the subject matter that is present in the second particular audio content segment not being heard spoken by the second individual.
 20. The non-transitory computer-readable medium of claim 15, wherein processing each audio content segment of the first plurality of audio content segments using the multilabel classification machine-learning model to generate the first feature representation comprises: processing the audio content segment using a speech-to-text engine to generate a text representation of the audio content segment, the text representation comprising a sequence of words spoken in the audio content segment; and processing the text representation using the multilabel classification machine-learning model to generate the first feature representation. 